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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement guardrails and accountability	10–15%	- Autonomy and risk levels 1. Classify agent actions by risk 2. Assign autonomy levels with compliance constraints - Guardrails and human-in-the-loop 1. Require approvals for sensitive actions 2. Enforce least-privilege execution
Evaluation, error analysis, and tuning	15–20%	- Failure analysis 1. Classify reasoning, tool, and context errors 2. Analyze logs, traces, and artifacts - Define evaluation criteria 1. Generate automated evaluation signals 2. Define success metrics and constraints - Tuning agent behavior 1. Optimize memory usage and constraints 2. Refine prompts, tools, and workflows
Implement tool use and environment interaction	20–25%	- Safe execution and error handling 1. Escalation paths and traceability 2. Retries and rollback strategies - Development environment integration 1. Enable CI-based agent execution 2. Scope agents to repositories or branches 3. Enable autonomous actions (PRs, branches) - MCP server configuration 1. Add MCP servers to agents 2. Configure registries and allow lists - Agent tool configuration 1. Select and configure tools 2. Configure tool permissions and scope
Orchestrate multi-agent coordination	15–20%	- Failure handling and recovery 1. Detect stalled or degraded agents 2. Implement rollback and recovery patterns - Lifecycle management 1. Add/replace/retire agents safely - Multi-agent workflows 1. Resolve conflicts and overlaps 2. Coordinate parallel agent execution - Observability and auditability 1. Document agent handoffs and decisions 2. Generate logs and artifacts for review
Prepare agent architecture and SDLC processes	15–20%	- Observability and control 1. Produce inspectable artifacts in GitHub 2. Define autonomy levels and guardrails 3. Enable human-in-the-loop controls - Planning vs execution boundaries 1. Prevent execution before approval 2. Validate structured agent plans 3. Separate planning and execution phases - Integrate agents into SDLC workflows 1. Define inputs, outputs, and success criteria 2. Define agent steps in SDLC 3. Identify and mitigate agent anti-patterns
Manage memory, state, and execution	10–15%	- Agent memory strategies 1. Short-term vs long-term memory selection 2. Memory scoping and expiration rules - State persistence and drift control 1. Persist task progress as artifacts 2. Detect and correct context drift - Cross-tool continuity 1. Prevent stale or conflicting context 2. Share state across tools and environments

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Microsoft GitHub Agentic AI Developer Sample Questions (Q60-Q65):

NEW QUESTION # 60
After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?

A. --allow-all
B. /compact
C. .copilotignore
D. /clear

Answer: B

Explanation:
/compact summarizes the existing conversation history via the model and replaces the raw history with the condensed summary, preserving continuity and prior progress while freeing up context window space -- unlike /clear, which discards everything.

NEW QUESTION # 61
An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?

A. /clear
B. /compact
C. /usage
D. /reset-context

Answer: A

Explanation:
/clear wipes the entire conversation history and starts a fresh session. This differs from /compact, which summarizes history to save tokens while retaining prior progress.

NEW QUESTION # 62
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.

A. action
B. user
C. actor_is_agent
D. agent_session_id

Answer: C,D

NEW QUESTION # 63
You use the GitHub Copilot CLI in ephemeral dev containers.
You need to provide Copilot with reusable guidance for a specific task only. The guidance must be stored in the repository and invoked only when the task is relevant.
What should you do?

A. Create a skill under .copilot/skills/<skill-name>/SKILL.md.
B. Create a skill under .github/skills/<skill-name>/SKILL.md.
C. Add personal custom instructions to the user's Copilot settings.
D. Add repository-wide custom instructions to .github/copilot-instructions.md.

Answer: B

Explanation:
A repository skill stored under .github/skills/<skill-name>/SKILL.md provides reusable, task-specific guidance that can be invoked when relevant. Because it is committed to the repository, the guidance is available in ephemeral development containers without relying on a developer's local configuration.
Repository-wide instructions in .github/copilot-instructions.md are applied broadly and are suitable for enduring conventions, architectural rules, or team standards. They are not the best choice when guidance should apply only to a particular task or workflow.
Personal instructions are user-specific and do not provide a version-controlled, team-shared implementation. A .copilot/skills path does not represent the repository-scoped skill location required by the scenario.
Task-specific skills improve precision by supplying focused procedures, reference material, and constraints only when the related task is active. This prevents unrelated work from being burdened by instructions that do not apply.
Study-guide topics: agent skills, repository-scoped guidance, contextual instructions, and ephemeral development environments.

NEW QUESTION # 64
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Add clear, reproducible steps and expected behavior to the issue
B. Increase MCP server rate limits
C. Enable Copilot memory
D. Switch the agent to --allow-all mode

Answer: A

Explanation:
Agents rely entirely on the context provided in the issue. Vague issues lead to incorrect fixes.
Providing reproduction steps, error logs, and expected behavior gives the agent the information it needs to correctly diagnose and resolve the root cause.

NEW QUESTION # 65
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